Overview

Dataset statistics

Number of variables40
Number of observations847102
Missing cells96
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory366.5 MiB
Average record size in memory453.6 B

Variable types

CAT20
NUM15
DATE5

Warnings

QU has constant value "847102" Constant
FOLIO has a high cardinality: 377431 distinct values High cardinality
SITE_ADDR has a high cardinality: 376952 distinct values High cardinality
SITE_CITY has a high cardinality: 66 distinct values High cardinality
SITE_ZIP has a high cardinality: 2220 distinct values High cardinality
SD1 has a high cardinality: 169 distinct values High cardinality
NBHC has a high cardinality: 313 distinct values High cardinality
LAND_TYPE_ID has a high cardinality: 8814 distinct values High cardinality
BLOCK_NUM has a high cardinality: 887 distinct values High cardinality
LOT_NUM has a high cardinality: 19649 distinct values High cardinality
BLDG is highly correlated with JUST and 1 other fieldsHigh correlation
JUST is highly correlated with BLDG and 2 other fieldsHigh correlation
ASD_VAL is highly correlated with JUST and 2 other fieldsHigh correlation
TAX_VAL is highly correlated with JUST and 1 other fieldsHigh correlation
S_AMT is highly skewed (γ1 = 21.81267624) Skewed
ACREAGE is highly skewed (γ1 = 41.55003936) Skewed
FOLIO is uniformly distributed Uniform
SITE_ADDR is uniformly distributed Uniform
df_index has unique values Unique
tBLDGS has 168348 (19.9%) zeros Zeros
EXF has 316973 (37.4%) zeros Zeros
TAX_VAL has 21462 (2.5%) zeros Zeros

Reproduction

Analysis started2022-02-24 21:18:16.069021
Analysis finished2022-02-24 21:19:57.891115
Duration1 minute and 41.82 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct847102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean973128.6992
Minimum8
Maximum2047227
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:19:58.080655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile91386.05
Q1442455
median963574
Q31480502.75
95-th percentile1944098.9
Maximum2047227
Range2047219
Interquartile range (IQR)1038047.75

Descriptive statistics

Standard deviation590885.1658
Coefficient of variation (CV)0.6072014589
Kurtosis-1.180949512
Mean973128.6992
Median Absolute Deviation (MAD)519329.5
Skewness0.09236690488
Sum8.243392673e+11
Variance3.491452792e+11
MonotocityStrictly increasing
2022-02-24T16:19:58.158294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81< 0.1%
 
12901311< 0.1%
 
12900931< 0.1%
 
12900951< 0.1%
 
12901001< 0.1%
 
12901011< 0.1%
 
12901071< 0.1%
 
12901081< 0.1%
 
12901091< 0.1%
 
12901151< 0.1%
 
12901261< 0.1%
 
12901301< 0.1%
 
12901351< 0.1%
 
12900901< 0.1%
 
12901371< 0.1%
 
12901381< 0.1%
 
12901401< 0.1%
 
12901421< 0.1%
 
12901431< 0.1%
 
12901521< 0.1%
 
12901551< 0.1%
 
12901571< 0.1%
 
12901581< 0.1%
 
12901591< 0.1%
 
12900911< 0.1%
 
Other values (847077)847077> 99.9%
 
ValueCountFrequency (%) 
81< 0.1%
 
91< 0.1%
 
111< 0.1%
 
141< 0.1%
 
201< 0.1%
 
211< 0.1%
 
221< 0.1%
 
231< 0.1%
 
241< 0.1%
 
251< 0.1%
 
ValueCountFrequency (%) 
20472271< 0.1%
 
20472261< 0.1%
 
20472231< 0.1%
 
20472221< 0.1%
 
20472201< 0.1%
 
20472141< 0.1%
 
20472131< 0.1%
 
20472111< 0.1%
 
20472081< 0.1%
 
20472011< 0.1%
 

FOLIO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct377431
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
1219430000
 
12
1488210176
 
11
0045299115
 
11
0036808582
 
11
0190301866
 
10
Other values (377426)
847047 
ValueCountFrequency (%) 
121943000012< 0.1%
 
148821017611< 0.1%
 
004529911511< 0.1%
 
003680858211< 0.1%
 
019030186610< 0.1%
 
118366000010< 0.1%
 
016123751210< 0.1%
 
003680844210< 0.1%
 
126415000010< 0.1%
 
014525243610< 0.1%
 
067476000010< 0.1%
 
027554762210< 0.1%
 
186317502610< 0.1%
 
054952341210< 0.1%
 
142577009010< 0.1%
 
005231715610< 0.1%
 
072369000010< 0.1%
 
120369000010< 0.1%
 
146209000010< 0.1%
 
057472360810< 0.1%
 
036675504610< 0.1%
 
157947000010< 0.1%
 
180030000010< 0.1%
 
149926010010< 0.1%
 
148172010010< 0.1%
 
Other values (377406)846847> 99.9%
 
2022-02-24T16:19:58.941123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique136797 ?
Unique (%)16.1%
2022-02-24T16:19:59.012880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8471020100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common8471020100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8471020100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

DOR_CODE
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.7 KiB
0100
655468 
0400
92814 
0106
73653 
0200
 
16019
0800
 
8243
Other values (3)
 
905
ValueCountFrequency (%) 
010065546877.4%
 
04009281411.0%
 
0106736538.7%
 
0200160191.9%
 
080082431.0%
 
04087580.1%
 
0801107< 0.1%
 
010240< 0.1%
 
2022-02-24T16:19:59.077255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:19:59.123105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:59.185105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3388408100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3388408100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3388408100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

S_DATE
Date

Distinct8149
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1980-01-01 00:00:00
Maximum2022-01-28 00:00:00
2022-02-24T16:19:59.257068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:59.345665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VI
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.5 KiB
I
799492 
V
 
47610
ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 
2022-02-24T16:19:59.428208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:19:59.471239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:59.514105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

QU
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.5 KiB
Q
847102 
ValueCountFrequency (%) 
Q847102100.0%
 
2022-02-24T16:19:59.573896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:19:59.612778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:59.652628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters1
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Q847102100.0%
 

REA_CD
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.6 KiB
01
543638 
02
255255 
2A
 
15502
2B
 
15440
00
 
14524
Other values (21)
 
2743
ValueCountFrequency (%) 
0154363864.2%
 
0225525530.1%
 
2A155021.8%
 
2B154401.8%
 
00145241.7%
 
3C17290.2%
 
3B243< 0.1%
 
38229< 0.1%
 
05205< 0.1%
 
3297< 0.1%
 
3A59< 0.1%
 
3D57< 0.1%
 
3733< 0.1%
 
1821< 0.1%
 
1219< 0.1%
 
3016< 0.1%
 
1115< 0.1%
 
206< 0.1%
 
193< 0.1%
 
342< 0.1%
 
352< 0.1%
 
142< 0.1%
 
982< 0.1%
 
131< 0.1%
 
211< 0.1%
 
2022-02-24T16:19:59.721900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2022-02-24T16:19:59.786674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
082816948.9%
 
154371532.1%
 
228632016.9%
 
B156830.9%
 
A155610.9%
 
324680.1%
 
C17290.1%
 
8252< 0.1%
 
5207< 0.1%
 
D57< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number166117498.1%
 
Uppercase Letter330301.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
082816949.9%
 
154371532.7%
 
228632017.2%
 
324680.1%
 
8252< 0.1%
 
5207< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1568347.5%
 
A1556147.1%
 
C17295.2%
 
D570.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common166117498.1%
 
Latin330301.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
082816949.9%
 
154371532.7%
 
228632017.2%
 
324680.1%
 
8252< 0.1%
 
5207< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B1568347.5%
 
A1556147.1%
 
C17295.2%
 
D570.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
082816948.9%
 
154371532.1%
 
228632016.9%
 
B156830.9%
 
A155610.9%
 
324680.1%
 
C17290.1%
 
8252< 0.1%
 
5207< 0.1%
 
D57< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

S_AMT
Real number (ℝ≥0)

SKEWED

Distinct11127
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198455.6187
Minimum1100
Maximum26433000
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:19:59.856448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile32000
Q176600
median142000
Q3235000
95-th percentile465000
Maximum26433000
Range26431900
Interquartile range (IQR)158400

Descriptive statistics

Standard deviation393473.7381
Coefficient of variation (CV)1.98267875
Kurtosis686.9227945
Mean198455.6187
Median Absolute Deviation (MAD)73500
Skewness21.81267624
Sum1.681121515e+11
Variance1.548215825e+11
MonotocityNot monotonic
2022-02-24T16:19:59.941160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12500049880.6%
 
6500048440.6%
 
15000048310.6%
 
7500047950.6%
 
8500045040.5%
 
13500044090.5%
 
5000043940.5%
 
5500043030.5%
 
17500042910.5%
 
12000042870.5%
 
11000042620.5%
 
6000042610.5%
 
13000041880.5%
 
11500041600.5%
 
16500041360.5%
 
14000041190.5%
 
8000040680.5%
 
16000040660.5%
 
14500039920.5%
 
4500039850.5%
 
9000039670.5%
 
20000039660.5%
 
10000038670.5%
 
7000038130.5%
 
15500037800.4%
 
Other values (11102)74082687.5%
 
ValueCountFrequency (%) 
11001< 0.1%
 
11131< 0.1%
 
12007< 0.1%
 
13005< 0.1%
 
13751< 0.1%
 
14002< 0.1%
 
150013< 0.1%
 
15241< 0.1%
 
16002< 0.1%
 
16551< 0.1%
 
ValueCountFrequency (%) 
264330001< 0.1%
 
225195001< 0.1%
 
217350001< 0.1%
 
1688750040< 0.1%
 
1659360030< 0.1%
 
165036003< 0.1%
 
149407001< 0.1%
 
1358090053< 0.1%
 
128000001< 0.1%
 
117000001< 0.1%
 

S_TYPE
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.1 KiB
WD
834201 
TR
 
6967
AG
 
2002
AD
 
1109
FD
 
924
Other values (14)
 
1899
ValueCountFrequency (%) 
WD83420198.5%
 
TR69670.8%
 
AG20020.2%
 
AD11090.1%
 
FD9240.1%
 
QC7080.1%
 
CT4480.1%
 
00225< 0.1%
 
DD119< 0.1%
 
PR103< 0.1%
 
GD98< 0.1%
 
AS89< 0.1%
 
CD38< 0.1%
 
TD31< 0.1%
 
MD18< 0.1%
 
ED18< 0.1%
 
WQ2< 0.1%
 
SD1< 0.1%
 
WS1< 0.1%
 
2022-02-24T16:20:00.021819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2022-02-24T16:20:00.086670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.2%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
0450< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1693754> 99.9%
 
Decimal Number450< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.3%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0450100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1693754> 99.9%
 
Common450< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.3%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0450100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.2%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
0450< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 
Distinct6235
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1901-12-01 00:00:00
Maximum2022-01-19 00:00:00
2022-02-24T16:20:00.156610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:00.239355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SITE_ADDR
Categorical

HIGH CARDINALITY
UNIFORM

Distinct376952
Distinct (%)44.5%
Missing89
Missing (%)< 0.1%
Memory size6.5 MiB
611 DESTINY DR
 
233
4201 BAYSHORE BLVD
 
132
2001 E 2ND AVE
 
104
3507 BAYSHORE BLVD
 
76
0
 
36
Other values (376947)
846432 
ValueCountFrequency (%) 
611 DESTINY DR233< 0.1%
 
4201 BAYSHORE BLVD132< 0.1%
 
2001 E 2ND AVE104< 0.1%
 
3507 BAYSHORE BLVD76< 0.1%
 
036< 0.1%
 
3119 W DELEON ST27< 0.1%
 
1002 CHANNELSIDE DR22< 0.1%
 
1022 BELLASOL WAY12< 0.1%
 
902 S ROME AVE12< 0.1%
 
5026 W DICKENS AVE12< 0.1%
 
8523 J R MANOR DR11< 0.1%
 
12415 MONDRAGON DR11< 0.1%
 
7130 WATERSIDE DR11< 0.1%
 
16012 MARSHFIELD DR10< 0.1%
 
8601 N 39TH ST10< 0.1%
 
3109 W HAWTHORNE RD10< 0.1%
 
13404 PINE LAKE WAY10< 0.1%
 
1604 E NOME ST10< 0.1%
 
1026 BELLASOL WAY10< 0.1%
 
1705 E CHELSEA ST10< 0.1%
 
4911 E TEMPLE HEIGHTS RD A D10< 0.1%
 
18416 ORIOLE ST10< 0.1%
 
301 KNOTTWOOD CT10< 0.1%
 
5307 ABINGER CT10< 0.1%
 
136 BUTLER RD10< 0.1%
 
Other values (376927)84619499.9%
 
(Missing)89< 0.1%
 
2022-02-24T16:20:00.948088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique136558 ?
Unique (%)16.1%
2022-02-24T16:20:01.031958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length18
Mean length18.40164703
Min length1

Overview of Unicode Properties

Unique unicode characters57
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
230848314.8%
 
R10393636.7%
 
E9799426.3%
 
A8702545.6%
 
18550995.5%
 
D6637674.3%
 
L6562184.2%
 
N6105843.9%
 
05716953.7%
 
O5696583.7%
 
S5478113.5%
 
T5392203.5%
 
I4885973.1%
 
24723463.0%
 
C3616832.3%
 
33500422.2%
 
43212652.1%
 
W2806731.8%
 
52732821.8%
 
H2570461.6%
 
62478751.6%
 
V2441311.6%
 
82260671.5%
 
B2248701.4%
 
72221581.4%
 
Other values (32)14059439.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter953515261.2%
 
Decimal Number374228324.0%
 
Space Separator230848314.8%
 
Dash Punctuation938< 0.1%
 
Other Punctuation837< 0.1%
 
Lowercase Letter377< 0.1%
 
Modifier Symbol1< 0.1%
 
Open Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
185509922.8%
 
057169515.3%
 
247234612.6%
 
33500429.4%
 
43212658.6%
 
52732827.3%
 
62478756.6%
 
82260676.0%
 
72221585.9%
 
92024545.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2308483100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R103936310.9%
 
E97994210.3%
 
A8702549.1%
 
D6637677.0%
 
L6562186.9%
 
N6105846.4%
 
O5696586.0%
 
S5478115.7%
 
T5392205.7%
 
I4885975.1%
 
C3616833.8%
 
W2806732.9%
 
H2570462.7%
 
V2441312.6%
 
B2248702.4%
 
P2030872.1%
 
G1943222.0%
 
M1797811.9%
 
Y1783001.9%
 
K1679741.8%
 
U1448391.5%
 
F924761.0%
 
J135660.1%
 
X110030.1%
 
Z97410.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n19150.7%
 
a10026.5%
 
s174.5%
 
r143.7%
 
i123.2%
 
d112.9%
 
e112.9%
 
o102.7%
 
t82.1%
 
u10.3%
 
h10.3%
 
g10.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-938100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/53563.9%
 
#23227.7%
 
&586.9%
 
.111.3%
 
,10.1%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin953552961.2%
 
Common605254338.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
230848338.1%
 
185509914.1%
 
05716959.4%
 
24723467.8%
 
33500425.8%
 
43212655.3%
 
52732824.5%
 
62478754.1%
 
82260673.7%
 
72221583.7%
 
92024543.3%
 
-938< 0.1%
 
/535< 0.1%
 
#232< 0.1%
 
&58< 0.1%
 
.11< 0.1%
 
,1< 0.1%
 
`1< 0.1%
 
[1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R103936310.9%
 
E97994210.3%
 
A8702549.1%
 
D6637677.0%
 
L6562186.9%
 
N6105846.4%
 
O5696586.0%
 
S5478115.7%
 
T5392205.7%
 
I4885975.1%
 
C3616833.8%
 
W2806732.9%
 
H2570462.7%
 
V2441312.6%
 
B2248702.4%
 
P2030872.1%
 
G1943222.0%
 
M1797811.9%
 
Y1783001.9%
 
K1679741.8%
 
U1448391.5%
 
F924761.0%
 
J135660.1%
 
X110030.1%
 
Z97410.1%
 
Other values (13)66230.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15588072100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
230848314.8%
 
R10393636.7%
 
E9799426.3%
 
A8702545.6%
 
18550995.5%
 
D6637674.3%
 
L6562184.2%
 
N6105843.9%
 
05716953.7%
 
O5696583.7%
 
S5478113.5%
 
T5392203.5%
 
I4885973.1%
 
24723463.0%
 
C3616832.3%
 
33500422.2%
 
43212652.1%
 
W2806731.8%
 
52732821.8%
 
H2570461.6%
 
62478751.6%
 
V2441311.6%
 
82260671.5%
 
B2248701.4%
 
72221581.4%
 
Other values (32)14059439.0%
 

SITE_CITY
Categorical

HIGH CARDINALITY

Distinct66
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size6.5 MiB
TAMPA
441024 
RIVERVIEW
75729 
BRANDON
52580 
VALRICO
49710 
SUN CITY CENTER
 
33048
Other values (61)
195004 
ValueCountFrequency (%) 
TAMPA44102452.1%
 
RIVERVIEW757298.9%
 
BRANDON525806.2%
 
VALRICO497105.9%
 
SUN CITY CENTER330483.9%
 
PLANT CITY328383.9%
 
LUTZ299603.5%
 
APOLLO BEACH215072.5%
 
RUSKIN194182.3%
 
LITHIA172692.0%
 
TEMPLE TERRACE169952.0%
 
SEFFNER158261.9%
 
ODESSA122221.4%
 
WIMAUMA107901.3%
 
GIBSONTON80190.9%
 
DOVER56340.7%
 
THONOTOSASSA35000.4%
 
Tampa4430.1%
 
Unincorporated249< 0.1%
 
LAKELAND138< 0.1%
 
Plant City51< 0.1%
 
Temple Terrace37< 0.1%
 
ZEPHYRHILLS30< 0.1%
 
MULBERRY9< 0.1%
 
WIMAUAM6< 0.1%
 
Other values (41)63< 0.1%
 
(Missing)7< 0.1%
 
2022-02-24T16:20:01.120049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique26 ?
Unique (%)< 0.1%
2022-02-24T16:20:01.192057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length5
Mean length6.69774006
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A113560620.0%
 
T66957811.8%
 
P5124639.0%
 
M4796608.5%
 
R3617616.4%
 
E3567676.3%
 
I3399146.0%
 
N2590424.6%
 
V2068303.6%
 
L1901543.4%
 
O1897323.3%
 
C1872223.3%
 
1375562.4%
 
S1113022.0%
 
U935051.6%
 
W865481.5%
 
B821211.4%
 
D705801.2%
 
Y659351.2%
 
H423500.7%
 
F316540.6%
 
Z299900.5%
 
K195570.3%
 
G80280.1%
 
a1230< 0.1%
 
Other values (21)45840.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter553030497.5%
 
Space Separator1375562.4%
 
Lowercase Letter57940.1%
 
Decimal Number13< 0.1%
 
Modifier Symbol2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A113560620.5%
 
T66957812.1%
 
P5124639.3%
 
M4796608.7%
 
R3617616.5%
 
E3567676.5%
 
I3399146.1%
 
N2590424.7%
 
V2068303.7%
 
L1901543.4%
 
O1897323.4%
 
C1872223.4%
 
S1113022.0%
 
U935051.7%
 
W865481.6%
 
B821211.5%
 
D705801.3%
 
Y659351.2%
 
H423500.8%
 
F316540.6%
 
Z299900.5%
 
K195570.4%
 
G80280.1%
 
Q5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a123021.2%
 
p72912.6%
 
r5729.9%
 
n5639.7%
 
o4988.6%
 
m4808.3%
 
e3976.9%
 
t3516.1%
 
i3005.2%
 
c2864.9%
 
d2494.3%
 
l881.5%
 
y510.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
137556100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3430.8%
 
8215.4%
 
2215.4%
 
0215.4%
 
517.7%
 
617.7%
 
917.7%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin553609897.6%
 
Common1375712.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A113560620.5%
 
T66957812.1%
 
P5124639.3%
 
M4796608.7%
 
R3617616.5%
 
E3567676.4%
 
I3399146.1%
 
N2590424.7%
 
V2068303.7%
 
L1901543.4%
 
O1897323.4%
 
C1872223.4%
 
S1113022.0%
 
U935051.7%
 
W865481.6%
 
B821211.5%
 
D705801.3%
 
Y659351.2%
 
H423500.8%
 
F316540.6%
 
Z299900.5%
 
K195570.4%
 
G80280.1%
 
a1230< 0.1%
 
p729< 0.1%
 
Other values (12)38400.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
137556> 99.9%
 
34< 0.1%
 
`2< 0.1%
 
82< 0.1%
 
22< 0.1%
 
02< 0.1%
 
51< 0.1%
 
61< 0.1%
 
91< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5673669100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A113560620.0%
 
T66957811.8%
 
P5124639.0%
 
M4796608.5%
 
R3617616.4%
 
E3567676.3%
 
I3399146.0%
 
N2590424.6%
 
V2068303.6%
 
L1901543.4%
 
O1897323.3%
 
C1872223.3%
 
1375562.4%
 
S1113022.0%
 
U935051.6%
 
W865481.5%
 
B821211.4%
 
D705801.2%
 
Y659351.2%
 
H423500.7%
 
F316540.6%
 
Z299900.5%
 
K195570.3%
 
G80280.1%
 
a1230< 0.1%
 
Other values (21)45840.1%
 

SITE_ZIP
Categorical

HIGH CARDINALITY

Distinct2220
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
33647
 
44244
33573
 
36526
33624
 
35826
33511
 
33575
33578
 
31755
Other values (2215)
665176 
ValueCountFrequency (%) 
33647442445.2%
 
33573365264.3%
 
33624358264.2%
 
33511335754.0%
 
33578317553.7%
 
33615293763.5%
 
33611258443.1%
 
33596253673.0%
 
33604247662.9%
 
33579246682.9%
 
33626244092.9%
 
33594237712.8%
 
33617235742.8%
 
33629226802.7%
 
33572211552.5%
 
33618199952.4%
 
33625196482.3%
 
33614192842.3%
 
33612191292.3%
 
33510185362.2%
 
33569176752.1%
 
33547170582.0%
 
33584155721.8%
 
33570148091.7%
 
33610147111.7%
 
Other values (2195)24314928.7%
 
2022-02-24T16:20:01.269203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique724 ?
Unique (%)0.1%
2022-02-24T16:20:01.338968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length5.04094076
Min length5

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number425976299.8%
 
Dash Punctuation104250.2%
 
Connector Punctuation4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3180717542.4%
 
660580614.2%
 
549295211.6%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-10425100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4270191100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4270191100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

tBEDS
Real number (ℝ≥0)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.123815432
Minimum0
Maximum24
Zeros3792
Zeros (%)0.4%
Memory size6.5 MiB
2022-02-24T16:20:01.398937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9717916442
Coefficient of variation (CV)0.311091249
Kurtosis1.263989668
Mean3.123815432
Median Absolute Deviation (MAD)1
Skewness0.1646001513
Sum2646190.3
Variance0.9443789998
MonotocityNot monotonic
2022-02-24T16:20:01.463743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
335684842.1%
 
422943627.1%
 
217767421.0%
 
5454905.4%
 
1259243.1%
 
666570.8%
 
037920.4%
 
79170.1%
 
8173< 0.1%
 
948< 0.1%
 
1030< 0.1%
 
1124< 0.1%
 
2.523< 0.1%
 
3.523< 0.1%
 
0.311< 0.1%
 
139< 0.1%
 
1.59< 0.1%
 
128< 0.1%
 
5.53< 0.1%
 
151< 0.1%
 
241< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
037920.4%
 
0.311< 0.1%
 
1259243.1%
 
1.59< 0.1%
 
217767421.0%
 
2.523< 0.1%
 
335684842.1%
 
3.523< 0.1%
 
422943627.1%
 
5454905.4%
 
ValueCountFrequency (%) 
241< 0.1%
 
161< 0.1%
 
151< 0.1%
 
139< 0.1%
 
128< 0.1%
 
1124< 0.1%
 
1030< 0.1%
 
948< 0.1%
 
8173< 0.1%
 
79170.1%
 

tBATHS
Real number (ℝ≥0)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.222953552
Minimum0
Maximum17
Zeros3073
Zeros (%)0.4%
Memory size6.5 MiB
2022-02-24T16:20:01.794351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3.5
Maximum17
Range17
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.7817343878
Coefficient of variation (CV)0.3516647422
Kurtosis5.850254505
Mean2.222953552
Median Absolute Deviation (MAD)0
Skewness1.25144131
Sum1883068.4
Variance0.6111086531
MonotocityNot monotonic
2022-02-24T16:20:01.859139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
242434350.1%
 
2.512810615.1%
 
310831012.8%
 
19617911.4%
 
3.5284513.4%
 
1.5233612.8%
 
4192772.3%
 
4.577270.9%
 
536690.4%
 
030730.4%
 
5.522780.3%
 
68660.1%
 
6.57250.1%
 
7285< 0.1%
 
7.5197< 0.1%
 
878< 0.1%
 
8.564< 0.1%
 
931< 0.1%
 
1117< 0.1%
 
10.516< 0.1%
 
9.512< 0.1%
 
12.57< 0.1%
 
106< 0.1%
 
0.56< 0.1%
 
14.54< 0.1%
 
Other values (5)14< 0.1%
 
ValueCountFrequency (%) 
030730.4%
 
0.56< 0.1%
 
19617911.4%
 
1.14< 0.1%
 
1.5233612.8%
 
242434350.1%
 
2.512810615.1%
 
310831012.8%
 
3.5284513.4%
 
4192772.3%
 
ValueCountFrequency (%) 
171< 0.1%
 
14.54< 0.1%
 
141< 0.1%
 
12.57< 0.1%
 
124< 0.1%
 
11.54< 0.1%
 
1117< 0.1%
 
10.516< 0.1%
 
106< 0.1%
 
9.512< 0.1%
 

tSTORIES
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.294692375
Minimum0
Maximum11
Zeros1896
Zeros (%)0.2%
Memory size6.5 MiB
2022-02-24T16:20:01.925916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5010750046
Coefficient of variation (CV)0.3870224421
Kurtosis3.0621503
Mean1.294692375
Median Absolute Deviation (MAD)0
Skewness1.482789943
Sum1096736.5
Variance0.2510761602
MonotocityNot monotonic
2022-02-24T16:20:01.981725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
160628871.6%
 
222232126.2%
 
3105391.2%
 
1.538910.5%
 
018960.2%
 
415820.2%
 
2.5334< 0.1%
 
3.585< 0.1%
 
582< 0.1%
 
4.533< 0.1%
 
633< 0.1%
 
5.56< 0.1%
 
75< 0.1%
 
113< 0.1%
 
82< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
018960.2%
 
160628871.6%
 
1.538910.5%
 
222232126.2%
 
2.5334< 0.1%
 
3105391.2%
 
3.585< 0.1%
 
415820.2%
 
4.533< 0.1%
 
582< 0.1%
 
ValueCountFrequency (%) 
113< 0.1%
 
101< 0.1%
 
91< 0.1%
 
82< 0.1%
 
75< 0.1%
 
633< 0.1%
 
5.56< 0.1%
 
582< 0.1%
 
4.533< 0.1%
 
415820.2%
 

tUNITS
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.024708359
Minimum0
Maximum9
Zeros3055
Zeros (%)0.4%
Memory size6.5 MiB
2022-02-24T16:20:02.038549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.245855821
Coefficient of variation (CV)0.2399276037
Kurtosis233.9756271
Mean1.024708359
Median Absolute Deviation (MAD)0
Skewness12.08569953
Sum868032.5
Variance0.06044508471
MonotocityNot monotonic
2022-02-24T16:20:02.091553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
182668897.6%
 
2140741.7%
 
030550.4%
 
417810.2%
 
310600.1%
 
5139< 0.1%
 
8118< 0.1%
 
6114< 0.1%
 
741< 0.1%
 
931< 0.1%
 
3.51< 0.1%
 
ValueCountFrequency (%) 
030550.4%
 
182668897.6%
 
2140741.7%
 
310600.1%
 
3.51< 0.1%
 
417810.2%
 
5139< 0.1%
 
6114< 0.1%
 
741< 0.1%
 
8118< 0.1%
 
ValueCountFrequency (%) 
931< 0.1%
 
8118< 0.1%
 
741< 0.1%
 
6114< 0.1%
 
5139< 0.1%
 
417810.2%
 
3.51< 0.1%
 
310600.1%
 
2140741.7%
 
182668897.6%
 

tBLDGS
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8140684357
Minimum0
Maximum9
Zeros168348
Zeros (%)19.9%
Memory size6.5 MiB
2022-02-24T16:20:02.147375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4235400688
Coefficient of variation (CV)0.5202757535
Kurtosis2.087145125
Mean0.8140684357
Median Absolute Deviation (MAD)0
Skewness-0.909700508
Sum689599
Variance0.1793861899
MonotocityNot monotonic
2022-02-24T16:20:02.205300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
166865378.9%
 
016834819.9%
 
295431.1%
 
34290.1%
 
496< 0.1%
 
519< 0.1%
 
67< 0.1%
 
75< 0.1%
 
91< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
016834819.9%
 
166865378.9%
 
295431.1%
 
34290.1%
 
496< 0.1%
 
519< 0.1%
 
67< 0.1%
 
75< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
81< 0.1%
 
75< 0.1%
 
67< 0.1%
 
519< 0.1%
 
496< 0.1%
 
34290.1%
 
295431.1%
 
166865378.9%
 
016834819.9%
 

JUST
Real number (ℝ≥0)

HIGH CORRELATION

Distinct233533
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294145.4092
Minimum3650
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:20:02.341825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3650
5-th percentile88810
Q1179629
median246876.5
Q3340266.75
95-th percentile625102
Maximum16539559
Range16535909
Interquartile range (IQR)160637.75

Descriptive statistics

Standard deviation240877.7905
Coefficient of variation (CV)0.8189071901
Kurtosis249.1760728
Mean294145.4092
Median Absolute Deviation (MAD)77513.5
Skewness8.980293031
Sum2.491711644e+11
Variance5.802210995e+10
MonotocityNot monotonic
2022-02-24T16:20:02.423775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
74374398< 0.1%
 
71960373< 0.1%
 
153411369< 0.1%
 
56227362< 0.1%
 
89109361< 0.1%
 
65626347< 0.1%
 
73493336< 0.1%
 
102194317< 0.1%
 
73014302< 0.1%
 
161358294< 0.1%
 
66309287< 0.1%
 
68001285< 0.1%
 
103539278< 0.1%
 
55846277< 0.1%
 
118750274< 0.1%
 
60777274< 0.1%
 
146415234< 0.1%
 
77703230< 0.1%
 
45562230< 0.1%
 
82949228< 0.1%
 
30226228< 0.1%
 
80766225< 0.1%
 
47602222< 0.1%
 
47821219< 0.1%
 
111164218< 0.1%
 
Other values (233508)83993499.2%
 
ValueCountFrequency (%) 
365016< 0.1%
 
385022< 0.1%
 
400817< 0.1%
 
41848< 0.1%
 
42222< 0.1%
 
42663< 0.1%
 
443958< 0.1%
 
45811< 0.1%
 
47554< 0.1%
 
480054< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
122213255< 0.1%
 
109645312< 0.1%
 
95577754< 0.1%
 
93023244< 0.1%
 
87427943< 0.1%
 
82508801< 0.1%
 
78764261< 0.1%
 
76938662< 0.1%
 
75440332< 0.1%
 

LAND
Real number (ℝ≥0)

Distinct93658
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78944.39593
Minimum75
Maximum8673113
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:20:02.530236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile100
Q140859
median64260
Q391072
95-th percentile209664
Maximum8673113
Range8673038
Interquartile range (IQR)50213

Descriptive statistics

Standard deviation99046.77913
Coefficient of variation (CV)1.254639775
Kurtosis537.8256382
Mean78944.39593
Median Absolute Deviation (MAD)24870
Skewness13.5328729
Sum6.687395568e+10
Variance9810264456
MonotocityNot monotonic
2022-02-24T16:20:02.609970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1009343511.0%
 
8000018780.2%
 
21000017400.2%
 
3939015130.2%
 
5049013970.2%
 
6300013540.2%
 
20000013150.2%
 
5940012920.2%
 
3250012080.1%
 
6732011950.1%
 
4158010840.1%
 
12480010760.1%
 
5865010160.1%
 
468009550.1%
 
550008910.1%
 
693008890.1%
 
699938650.1%
 
550808550.1%
 
583288350.1%
 
328258160.1%
 
334758160.1%
 
720007890.1%
 
727207780.1%
 
499957710.1%
 
714007560.1%
 
Other values (93633)72758385.9%
 
ValueCountFrequency (%) 
7537< 0.1%
 
1009343511.0%
 
4021< 0.1%
 
6527< 0.1%
 
6543< 0.1%
 
9992< 0.1%
 
11951< 0.1%
 
13174< 0.1%
 
13291< 0.1%
 
13422< 0.1%
 
ValueCountFrequency (%) 
86731132< 0.1%
 
65022035< 0.1%
 
64621574< 0.1%
 
62541062< 0.1%
 
49655034< 0.1%
 
47736002< 0.1%
 
42436391< 0.1%
 
40202303< 0.1%
 
37444905< 0.1%
 
36732381< 0.1%
 

BLDG
Real number (ℝ≥0)

HIGH CORRELATION

Distinct196456
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206857.749
Minimum0
Maximum9840672
Zeros3111
Zeros (%)0.4%
Memory size6.5 MiB
2022-02-24T16:20:02.741529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68236
Q1130541
median175362
Q3238370.25
95-th percentile432959
Maximum9840672
Range9840672
Interquartile range (IQR)107829.25

Descriptive statistics

Standard deviation158482.9992
Coefficient of variation (CV)0.7661448504
Kurtosis173.734571
Mean206857.749
Median Absolute Deviation (MAD)51792.5
Skewness7.649765441
Sum1.752296129e+11
Variance2.511686102e+10
MonotocityNot monotonic
2022-02-24T16:20:02.826440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031110.4%
 
74274398< 0.1%
 
71860375< 0.1%
 
153311369< 0.1%
 
56127362< 0.1%
 
89009356< 0.1%
 
63987347< 0.1%
 
73393336< 0.1%
 
102094317< 0.1%
 
161258300< 0.1%
 
72914299< 0.1%
 
66209287< 0.1%
 
103439285< 0.1%
 
67901285< 0.1%
 
118650279< 0.1%
 
54207277< 0.1%
 
60677274< 0.1%
 
131773236< 0.1%
 
77603234< 0.1%
 
45462230< 0.1%
 
30126229< 0.1%
 
80387229< 0.1%
 
79490225< 0.1%
 
46914222< 0.1%
 
47721221< 0.1%
 
Other values (196431)83701998.8%
 
ValueCountFrequency (%) 
031110.4%
 
5162< 0.1%
 
5233< 0.1%
 
6333< 0.1%
 
7172< 0.1%
 
8091< 0.1%
 
8102< 0.1%
 
8711< 0.1%
 
8792< 0.1%
 
9222< 0.1%
 
ValueCountFrequency (%) 
98406724< 0.1%
 
70642304< 0.1%
 
66046162< 0.1%
 
58766471< 0.1%
 
56157695< 0.1%
 
52846203< 0.1%
 
50675221< 0.1%
 
49170342< 0.1%
 
49070071< 0.1%
 
47542751< 0.1%
 

EXF
Real number (ℝ≥0)

ZEROS

Distinct34660
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8274.239497
Minimum0
Maximum409781
Zeros316973
Zeros (%)37.4%
Memory size6.5 MiB
2022-02-24T16:20:02.922712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1361
Q312173
95-th percentile35171
Maximum409781
Range409781
Interquartile range (IQR)12173

Descriptive statistics

Standard deviation13634.71872
Coefficient of variation (CV)1.647851592
Kurtosis20.07374653
Mean8274.239497
Median Absolute Deviation (MAD)1361
Skewness2.871606987
Sum7009124826
Variance185905554.7
MonotocityNot monotonic
2022-02-24T16:20:03.013916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031697337.4%
 
2502107091.3%
 
262736720.4%
 
950032830.4%
 
275229950.4%
 
2531226000.3%
 
300225290.3%
 
1335024160.3%
 
2467123480.3%
 
200219900.2%
 
1585218050.2%
 
2371016710.2%
 
2252716210.2%
 
287715330.2%
 
1602014940.2%
 
312814760.2%
 
760014340.2%
 
330314150.2%
 
2109314000.2%
 
1975813960.2%
 
2055913790.2%
 
1902213720.2%
 
1200213240.2%
 
2274813000.2%
 
2395912840.2%
 
Other values (34635)47568356.2%
 
ValueCountFrequency (%) 
031697337.4%
 
51< 0.1%
 
62< 0.1%
 
92< 0.1%
 
152< 0.1%
 
162< 0.1%
 
186< 0.1%
 
416< 0.1%
 
422< 0.1%
 
573< 0.1%
 
ValueCountFrequency (%) 
4097811< 0.1%
 
3774421< 0.1%
 
3668303< 0.1%
 
3549261< 0.1%
 
3278141< 0.1%
 
3145271< 0.1%
 
2853851< 0.1%
 
2850011< 0.1%
 
2847451< 0.1%
 
2845223< 0.1%
 

HEAT_AR
Real number (ℝ≥0)

Distinct6163
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1860.356963
Minimum0
Maximum28893
Zeros1813
Zeros (%)0.2%
Memory size6.5 MiB
2022-02-24T16:20:03.103696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile890
Q11283
median1674
Q32237
95-th percentile3424
Maximum28893
Range28893
Interquartile range (IQR)954

Descriptive statistics

Standard deviation853.5299338
Coefficient of variation (CV)0.4587990105
Kurtosis15.15899919
Mean1860.356963
Median Absolute Deviation (MAD)443
Skewness2.164527077
Sum1575912104
Variance728513.348
MonotocityNot monotonic
2022-02-24T16:20:03.186112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120038380.5%
 
96035050.4%
 
115226680.3%
 
129622470.3%
 
91222440.3%
 
124822390.3%
 
151621550.3%
 
118420260.2%
 
126020230.2%
 
80019520.2%
 
144019520.2%
 
140418640.2%
 
127218490.2%
 
134418470.2%
 
108018170.2%
 
018130.2%
 
98417500.2%
 
117616730.2%
 
124416480.2%
 
116415790.2%
 
110415530.2%
 
128015390.2%
 
140015310.2%
 
151215190.2%
 
140815040.2%
 
Other values (6138)79676794.1%
 
ValueCountFrequency (%) 
018130.2%
 
1401< 0.1%
 
1602< 0.1%
 
1922< 0.1%
 
2001< 0.1%
 
2162< 0.1%
 
2321< 0.1%
 
2401< 0.1%
 
2722< 0.1%
 
2803< 0.1%
 
ValueCountFrequency (%) 
288931< 0.1%
 
283631< 0.1%
 
217964< 0.1%
 
216381< 0.1%
 
189123< 0.1%
 
187032< 0.1%
 
185684< 0.1%
 
183993< 0.1%
 
177404< 0.1%
 
174001< 0.1%
 

ASD_VAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct224721
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211092.1663
Minimum2725
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:20:03.324765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2725
5-th percentile54005
Q1112457
median172631
Q3251333
95-th percentile478374.3
Maximum16539559
Range16536834
Interquartile range (IQR)138876

Descriptive statistics

Standard deviation193869.4509
Coefficient of variation (CV)0.918411395
Kurtosis404.3873584
Mean211092.1663
Median Absolute Deviation (MAD)67164
Skewness10.43269959
Sum1.788165963e+11
Variance3.7585364e+10
MonotocityNot monotonic
2022-02-24T16:20:03.404998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
92485244< 0.1%
 
74374210< 0.1%
 
56227194< 0.1%
 
117809193< 0.1%
 
30226188< 0.1%
 
71960180< 0.1%
 
80766164< 0.1%
 
100161154< 0.1%
 
157653142< 0.1%
 
30000140< 0.1%
 
59940139< 0.1%
 
146415136< 0.1%
 
119904134< 0.1%
 
38038127< 0.1%
 
66447127< 0.1%
 
95882126< 0.1%
 
45562125< 0.1%
 
111405125< 0.1%
 
66309124< 0.1%
 
61606120< 0.1%
 
45113118< 0.1%
 
92706118< 0.1%
 
65626107< 0.1%
 
47602105< 0.1%
 
126537104< 0.1%
 
Other values (224696)84345899.6%
 
ValueCountFrequency (%) 
27255< 0.1%
 
33533< 0.1%
 
35133< 0.1%
 
35417< 0.1%
 
365016< 0.1%
 
37523< 0.1%
 
385022< 0.1%
 
38932< 0.1%
 
39372< 0.1%
 
400817< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
105903845< 0.1%
 
87427943< 0.1%
 
81439044< 0.1%
 
74543331< 0.1%
 
73608962< 0.1%
 
72319771< 0.1%
 
65270161< 0.1%
 
63094414< 0.1%
 
61745021< 0.1%
 

TAX_VAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct213823
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175994.7366
Minimum0
Maximum16539559
Zeros21462
Zeros (%)2.5%
Memory size6.5 MiB
2022-02-24T16:20:03.540557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24447.05
Q174042.25
median137519
Q3219865
95-th percentile444675.9
Maximum16539559
Range16539559
Interquartile range (IQR)145822.75

Descriptive statistics

Standard deviation194630.6941
Coefficient of variation (CV)1.105889289
Kurtosis400.2275027
Mean175994.7366
Median Absolute Deviation (MAD)70393
Skewness10.33079586
Sum1.490854934e+11
Variance3.78811071e+10
MonotocityNot monotonic
2022-02-24T16:20:03.620289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25000306313.6%
 
0214622.5%
 
2450025180.3%
 
200004390.1%
 
92485244< 0.1%
 
74374199< 0.1%
 
117809195< 0.1%
 
30226189< 0.1%
 
56227188< 0.1%
 
71960171< 0.1%
 
100161154< 0.1%
 
80766153< 0.1%
 
59940147< 0.1%
 
157653142< 0.1%
 
30000135< 0.1%
 
66309131< 0.1%
 
111405130< 0.1%
 
45562127< 0.1%
 
61606126< 0.1%
 
38038125< 0.1%
 
66447124< 0.1%
 
119904124< 0.1%
 
146415124< 0.1%
 
95882123< 0.1%
 
45113119< 0.1%
 
Other values (213798)78888293.1%
 
ValueCountFrequency (%) 
0214622.5%
 
311< 0.1%
 
324< 0.1%
 
341< 0.1%
 
371< 0.1%
 
391< 0.1%
 
441< 0.1%
 
551< 0.1%
 
593< 0.1%
 
661< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
105403845< 0.1%
 
87427943< 0.1%
 
80939044< 0.1%
 
74543331< 0.1%
 
73108962< 0.1%
 
72319771< 0.1%
 
64770161< 0.1%
 
62594414< 0.1%
 
61745021< 0.1%
 

SD1
Categorical

HIGH CARDINALITY

Distinct169
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
000
635962 
702
 
16449
006
 
13048
154
 
11061
037
 
10568
Other values (164)
160014 
ValueCountFrequency (%) 
00063596275.1%
 
702164491.9%
 
006130481.5%
 
154110611.3%
 
037105681.2%
 
00574220.9%
 
04763340.7%
 
01258520.7%
 
04352960.6%
 
01146120.5%
 
04136090.4%
 
05331260.4%
 
03430780.4%
 
06330240.4%
 
YGR28690.3%
 
04427590.3%
 
06127120.3%
 
02126370.3%
 
09726250.3%
 
07724680.3%
 
11624430.3%
 
06224350.3%
 
05723810.3%
 
00221370.3%
 
00321140.2%
 
Other values (144)9008110.6%
 
2022-02-24T16:20:03.716966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:03.790720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0212026783.4%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 
Y28690.1%
 
G28690.1%
 
R28690.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number253269999.7%
 
Uppercase Letter86070.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0212026783.7%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y286933.3%
 
G286933.3%
 
R286933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common253269999.7%
 
Latin86070.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0212026783.7%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y286933.3%
 
G286933.3%
 
R286933.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0212026783.4%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 
Y28690.1%
 
G28690.1%
 
R28690.1%
 

SD2
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.7 KiB
000
842332 
201
 
3578
YGR
 
795
928
 
209
929
 
150
Other values (3)
 
38
ValueCountFrequency (%) 
00084233299.4%
 
20135780.4%
 
YGR7950.1%
 
928209< 0.1%
 
929150< 0.1%
 
70233< 0.1%
 
0073< 0.1%
 
1402< 0.1%
 
2022-02-24T16:20:03.853812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:03.896668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:03.956602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0253061599.6%
 
239700.2%
 
135800.1%
 
Y795< 0.1%
 
G795< 0.1%
 
R795< 0.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number253892199.9%
 
Uppercase Letter23850.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0253061599.7%
 
239700.2%
 
135800.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y79533.3%
 
G79533.3%
 
R79533.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common253892199.9%
 
Latin23850.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0253061599.7%
 
239700.2%
 
135800.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y79533.3%
 
G79533.3%
 
R79533.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0253061599.6%
 
239700.2%
 
135800.1%
 
Y795< 0.1%
 
G795< 0.1%
 
R795< 0.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

TIF
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.0 KiB
0
785718 
E
 
30826
9
 
14563
1
 
5445
C
 
2242
Other values (11)
 
8308
ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 
2022-02-24T16:20:04.027456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:04.094236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number81215495.9%
 
Uppercase Letter349484.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
078571896.7%
 
9145631.8%
 
154450.7%
 
618640.2%
 
213760.2%
 
312540.2%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
74< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E3082688.2%
 
C22426.4%
 
D14904.3%
 
A1910.5%
 
N1870.5%
 
B12< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common81215495.9%
 
Latin349484.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
078571896.7%
 
9145631.8%
 
154450.7%
 
618640.2%
 
213760.2%
 
312540.2%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
74< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E3082688.2%
 
C22426.4%
 
D14904.3%
 
A1910.5%
 
N1870.5%
 
B12< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 

ACREAGE
Real number (ℝ≥0)

SKEWED

Distinct217700
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2779287273
Minimum2.03714e-05
Maximum102.646
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-02-24T16:20:04.217427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.03714e-05
5-th percentile0.0100321
Q10.11460825
median0.1693355
Q30.250517
95-th percentile0.89392
Maximum102.646
Range102.6459796
Interquartile range (IQR)0.13590875

Descriptive statistics

Standard deviation0.6973655742
Coefficient of variation (CV)2.509152547
Kurtosis4767.892591
Mean0.2779287273
Median Absolute Deviation (MAD)0.0646365
Skewness41.55003936
Sum235433.9808
Variance0.4863187441
MonotocityNot monotonic
2022-02-24T16:20:04.297160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.16527e-05118921.4%
 
9.1827e-05110841.3%
 
5.1664e-0534670.4%
 
0.12626328980.3%
 
5.16414e-0528370.3%
 
0.13774119350.2%
 
0.12626216660.2%
 
0.1010114800.2%
 
0.12626411180.1%
 
0.1147849240.1%
 
0.1320028020.1%
 
0.01019297380.1%
 
0.1515157340.1%
 
0.137746870.1%
 
0.1388896490.1%
 
0.1010116420.1%
 
0.1262656150.1%
 
0.01019286060.1%
 
0.1377425970.1%
 
0.1652895930.1%
 
0.1262665520.1%
 
0.1262615500.1%
 
0.1101935390.1%
 
0.1147835060.1%
 
0.1010094980.1%
 
Other values (217675)79849394.3%
 
ValueCountFrequency (%) 
2.03714e-055< 0.1%
 
3.80899e-053< 0.1%
 
4.20554e-051< 0.1%
 
4.4168e-052< 0.1%
 
4.72805e-051< 0.1%
 
4.78615e-052< 0.1%
 
4.84166e-052< 0.1%
 
4.89977e-052< 0.1%
 
5.16177e-052< 0.1%
 
5.16219e-051< 0.1%
 
ValueCountFrequency (%) 
102.6468< 0.1%
 
52.23651< 0.1%
 
47.85982< 0.1%
 
41.41011< 0.1%
 
40.8991< 0.1%
 
40.30082< 0.1%
 
39.7321< 0.1%
 
39.6944< 0.1%
 
38.60562< 0.1%
 
37.2812< 0.1%
 

NBHC
Categorical

HIGH CARDINALITY

Distinct313
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
228003
 
20376
224005
 
15718
228004
 
11840
225001
 
11481
212003
 
10912
Other values (308)
776775 
ValueCountFrequency (%) 
228003203762.4%
 
224005157181.9%
 
228004118401.4%
 
225001114811.4%
 
212003109121.3%
 
21200494811.1%
 
22700192261.1%
 
22600283911.0%
 
22000383631.0%
 
21200681961.0%
 
22300873960.9%
 
20200173370.9%
 
20901272580.9%
 
22200665270.8%
 
22200264190.8%
 
21200862370.7%
 
22600162240.7%
 
22700361550.7%
 
22300161340.7%
 
22300960780.7%
 
22000459990.7%
 
21600258840.7%
 
21400658690.7%
 
22600356810.7%
 
22300756330.7%
 
Other values (288)63828775.3%
 
2022-02-24T16:20:04.390500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:04.467076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number592971487.5%
 
Other Punctuation84710212.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0273359646.1%
 
2141458123.9%
 
161495910.4%
 
32477214.2%
 
62209163.7%
 
51756723.0%
 
41684642.8%
 
71345082.3%
 
81254322.1%
 
9938651.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.847102100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6776816100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6776816100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

MUNICIPALITY_CD
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
U
589960 
A
220419 
P
 
20141
T
 
16582
ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 
2022-02-24T16:20:04.531859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:04.576709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:04.626131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

SECTION_CD
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.7 KiB
10
 
30219
06
 
29739
05
 
29452
07
 
29412
12
 
28773
Other values (31)
699507 
ValueCountFrequency (%) 
10302193.6%
 
06297393.5%
 
05294523.5%
 
07294123.5%
 
12287733.4%
 
17285273.4%
 
33280033.3%
 
04279793.3%
 
32263063.1%
 
11261053.1%
 
20256353.0%
 
08253223.0%
 
21243972.9%
 
14243112.9%
 
28240282.8%
 
23237392.8%
 
22231932.7%
 
18231702.7%
 
36227992.7%
 
26226882.7%
 
15226292.7%
 
13224862.7%
 
27223342.6%
 
25221952.6%
 
16218812.6%
 
Other values (11)21178025.0%
 
2022-02-24T16:20:04.706922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:04.779361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

TOWNSHIP_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
28
252681 
29
213671 
30
144807 
27
118262 
31
65403 
ValueCountFrequency (%) 
2825268129.8%
 
2921367125.2%
 
3014480717.1%
 
2711826214.0%
 
31654037.7%
 
32522786.2%
 
2022-02-24T16:20:04.848130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:04.897964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:04.956481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

RANGE_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
18
246935 
20
210497 
19
200799 
17
99602 
21
68313 
ValueCountFrequency (%) 
1824693529.2%
 
2021049724.8%
 
1920079923.7%
 
179960211.8%
 
21683138.1%
 
22209562.5%
 
2022-02-24T16:20:05.040302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-24T16:20:05.093484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:05.153590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

LAND_TYPE_ID
Categorical

HIGH CARDINALITY

Distinct8814
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
ZZZ
 
34656
3U4
 
2756
509
 
2677
42J
 
2188
3TP
 
1815
Other values (8809)
803010 
ValueCountFrequency (%) 
ZZZ346564.1%
 
3U427560.3%
 
50926770.3%
 
42J21880.3%
 
3TP18150.2%
 
45M16650.2%
 
3LA14840.2%
 
45414590.2%
 
10414100.2%
 
3TR13470.2%
 
4PQ11440.1%
 
3T710490.1%
 
98M10400.1%
 
1TM10250.1%
 
82010160.1%
 
88J10060.1%
 
36C9950.1%
 
0BJ9880.1%
 
89N9770.1%
 
8639640.1%
 
9D79600.1%
 
0609570.1%
 
3D69480.1%
 
82P9350.1%
 
09K9340.1%
 
Other values (8789)78070792.2%
 
2022-02-24T16:20:05.237490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique274 ?
Unique (%)< 0.1%
2022-02-24T16:20:05.313240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01886527.4%
 
21730876.8%
 
31704486.7%
 
11575586.2%
 
51424725.6%
 
41416425.6%
 
Z1405315.5%
 
91141384.5%
 
8914813.6%
 
7909413.6%
 
6878723.5%
 
A624622.5%
 
B561072.2%
 
P534952.1%
 
T466591.8%
 
U460851.8%
 
X460611.8%
 
W443981.7%
 
V436581.7%
 
C421981.7%
 
Q416941.6%
 
J409911.6%
 
F404771.6%
 
E401991.6%
 
Y398271.6%
 
Other values (11)39817315.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number135829153.4%
 
Uppercase Letter118301546.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
018865213.9%
 
217308712.7%
 
317044812.5%
 
115755811.6%
 
514247210.5%
 
414164210.4%
 
91141388.4%
 
8914816.7%
 
7909416.7%
 
6878726.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Z14053111.9%
 
A624625.3%
 
B561074.7%
 
P534954.5%
 
T466593.9%
 
U460853.9%
 
X460613.9%
 
W443983.8%
 
V436583.7%
 
C421983.6%
 
Q416943.5%
 
J409913.5%
 
F404773.4%
 
E401993.4%
 
Y398273.4%
 
D393593.3%
 
I388513.3%
 
H381683.2%
 
R379423.2%
 
S374553.2%
 
L356643.0%
 
N345822.9%
 
M345702.9%
 
G343662.9%
 
O342592.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135829153.4%
 
Latin118301546.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
018865213.9%
 
217308712.7%
 
317044812.5%
 
115755811.6%
 
514247210.5%
 
414164210.4%
 
91141388.4%
 
8914816.7%
 
7909416.7%
 
6878726.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Z14053111.9%
 
A624625.3%
 
B561074.7%
 
P534954.5%
 
T466593.9%
 
U460853.9%
 
X460613.9%
 
W443983.8%
 
V436583.7%
 
C421983.6%
 
Q416943.5%
 
J409913.5%
 
F404773.4%
 
E401993.4%
 
Y398273.4%
 
D393593.3%
 
I388513.3%
 
H381683.2%
 
R379423.2%
 
S374553.2%
 
L356643.0%
 
N345822.9%
 
M345702.9%
 
G343662.9%
 
O342592.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01886527.4%
 
21730876.8%
 
31704486.7%
 
11575586.2%
 
51424725.6%
 
41416425.6%
 
Z1405315.5%
 
91141384.5%
 
8914813.6%
 
7909413.6%
 
6878723.5%
 
A624622.5%
 
B561072.2%
 
P534952.1%
 
T466591.8%
 
U460851.8%
 
X460611.8%
 
W443981.7%
 
V436581.7%
 
C421981.7%
 
Q416941.6%
 
J409911.6%
 
F404771.6%
 
E401991.6%
 
Y398271.6%
 
Other values (11)39817315.7%
 

BLOCK_NUM
Categorical

HIGH CARDINALITY

Distinct887
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
000000
207148 
000001
100720 
000002
76191 
000003
56489 
000004
44817 
Other values (882)
361737 
ValueCountFrequency (%) 
00000020714824.5%
 
00000110072011.9%
 
000002761919.0%
 
000003564896.7%
 
000004448175.3%
 
000005362974.3%
 
000006249462.9%
 
000007193902.3%
 
000008167192.0%
 
A00000159091.9%
 
000009146881.7%
 
000010128181.5%
 
B00000124241.5%
 
000011102551.2%
 
C0000094371.1%
 
00001288431.0%
 
00001479750.9%
 
00001379160.9%
 
00001568750.8%
 
00001666330.8%
 
D0000063840.8%
 
00001760090.7%
 
00001846270.5%
 
E0000046120.5%
 
00001944570.5%
 
Other values (862)12452314.7%
 
2022-02-24T16:20:05.390979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20 ?
Unique (%)< 0.1%
2022-02-24T16:20:05.460756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0427240184.1%
 
12092694.1%
 
21342352.6%
 
3980461.9%
 
4740701.5%
 
5616891.2%
 
6475390.9%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 
A187480.4%
 
B161070.3%
 
C113810.2%
 
D77890.2%
 
E57860.1%
 
F37070.1%
 
G31990.1%
 
H2488< 0.1%
 
I2069< 0.1%
 
K1545< 0.1%
 
J1442< 0.1%
 
L1145< 0.1%
 
T941< 0.1%
 
N822< 0.1%
 
M804< 0.1%
 
Other values (11)43050.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number500033498.4%
 
Uppercase Letter822781.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0427240185.4%
 
12092694.2%
 
21342352.7%
 
3980462.0%
 
4740701.5%
 
5616891.2%
 
6475391.0%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1874822.8%
 
B1610719.6%
 
C1138113.8%
 
D77899.5%
 
E57867.0%
 
F37074.5%
 
G31993.9%
 
H24883.0%
 
I20692.5%
 
K15451.9%
 
J14421.8%
 
L11451.4%
 
T9411.1%
 
N8221.0%
 
M8041.0%
 
P7700.9%
 
S6940.8%
 
Q5950.7%
 
O5930.7%
 
R5560.7%
 
U3030.4%
 
V2730.3%
 
W2620.3%
 
X1560.2%
 
Y560.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common500033498.4%
 
Latin822781.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
0427240185.4%
 
12092694.2%
 
21342352.7%
 
3980462.0%
 
4740701.5%
 
5616891.2%
 
6475391.0%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A1874822.8%
 
B1610719.6%
 
C1138113.8%
 
D77899.5%
 
E57867.0%
 
F37074.5%
 
G31993.9%
 
H24883.0%
 
I20692.5%
 
K15451.9%
 
J14421.8%
 
L11451.4%
 
T9411.1%
 
N8221.0%
 
M8041.0%
 
P7700.9%
 
S6940.8%
 
Q5950.7%
 
O5930.7%
 
R5560.7%
 
U3030.4%
 
V2730.3%
 
W2620.3%
 
X1560.2%
 
Y560.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5082612100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0427240184.1%
 
12092694.1%
 
21342352.6%
 
3980461.9%
 
4740701.5%
 
5616891.2%
 
6475390.9%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 
A187480.4%
 
B161070.3%
 
C113810.2%
 
D77890.2%
 
E57860.1%
 
F37070.1%
 
G31990.1%
 
H2488< 0.1%
 
I2069< 0.1%
 
K1545< 0.1%
 
J1442< 0.1%
 
L1145< 0.1%
 
T941< 0.1%
 
N822< 0.1%
 
M804< 0.1%
 
Other values (11)43050.1%
 

LOT_NUM
Categorical

HIGH CARDINALITY

Distinct19649
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
00001.0
 
36125
00003.0
 
34674
00002.0
 
33764
00004.0
 
33354
00005.0
 
31269
Other values (19644)
677916 
ValueCountFrequency (%) 
00001.0361254.3%
 
00003.0346744.1%
 
00002.0337644.0%
 
00004.0333543.9%
 
00005.0312693.7%
 
00006.0294773.5%
 
00007.0265003.1%
 
00008.0253573.0%
 
00009.0227942.7%
 
00010.0215732.5%
 
00011.0209402.5%
 
00012.0190382.2%
 
00013.0187822.2%
 
00014.0174782.1%
 
00015.0168382.0%
 
00016.0158501.9%
 
00017.0152071.8%
 
00018.0140681.7%
 
00019.0134231.6%
 
00020.0124691.5%
 
00021.0117391.4%
 
00022.0111991.3%
 
00023.0104401.2%
 
00024.094661.1%
 
00025.086901.0%
 
Other values (19624)33658839.7%
 
2022-02-24T16:20:05.548449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3367 ?
Unique (%)0.4%
2022-02-24T16:20:05.624196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0355236559.9%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 
A45940.1%
 
B36190.1%
 
C2336< 0.1%
 
D1688< 0.1%
 
E1494< 0.1%
 
F867< 0.1%
 
G657< 0.1%
 
H563< 0.1%
 
W542< 0.1%
 
J410< 0.1%
 
L362< 0.1%
 
I341< 0.1%
 
K307< 0.1%
 
N286< 0.1%
 
Other values (10)1546< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number506300085.4%
 
Other Punctuation84710214.3%
 
Uppercase Letter196120.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0355236570.2%
 
13842557.6%
 
22461644.9%
 
31844053.6%
 
41495173.0%
 
51299042.6%
 
61169962.3%
 
71072542.1%
 
81008192.0%
 
9913211.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A459423.4%
 
B361918.5%
 
C233611.9%
 
D16888.6%
 
E14947.6%
 
F8674.4%
 
G6573.3%
 
H5632.9%
 
W5422.8%
 
J4102.1%
 
L3621.8%
 
I3411.7%
 
K3071.6%
 
N2861.5%
 
M2821.4%
 
S2661.4%
 
T2251.1%
 
P2121.1%
 
V1700.9%
 
O1270.6%
 
Q940.5%
 
R690.4%
 
X650.3%
 
U360.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common591010299.7%
 
Latin196120.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0355236560.1%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A459423.4%
 
B361918.5%
 
C233611.9%
 
D16888.6%
 
E14947.6%
 
F8674.4%
 
G6573.3%
 
H5632.9%
 
W5422.8%
 
J4102.1%
 
L3621.8%
 
I3411.7%
 
K3071.6%
 
N2861.5%
 
M2821.4%
 
S2661.4%
 
T2251.1%
 
P2121.1%
 
V1700.9%
 
O1270.6%
 
Q940.5%
 
R690.4%
 
X650.3%
 
U360.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5929714100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0355236559.9%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 
A45940.1%
 
B36190.1%
 
C2336< 0.1%
 
D1688< 0.1%
 
E1494< 0.1%
 
F867< 0.1%
 
G657< 0.1%
 
H563< 0.1%
 
W542< 0.1%
 
J410< 0.1%
 
L362< 0.1%
 
I341< 0.1%
 
K307< 0.1%
 
N286< 0.1%
 
Other values (10)1546< 0.1%
 
Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1860-01-01 00:00:00
Maximum2021-01-01 00:00:00
2022-02-24T16:20:05.688983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:05.771676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1900-01-01 00:00:00
Maximum2021-01-01 00:00:00
2022-02-24T16:20:05.853437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:05.934705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1901-01-01 00:00:00
Maximum2022-01-01 00:00:00
2022-02-24T16:20:06.023407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:20:06.103150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2022-02-24T16:19:00.403107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:00.632573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:00.856862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:01.081584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:01.309811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:01.527660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:01.736629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:01.950761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:02.168916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:02.386052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:02.599431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:02.815090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:03.030737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:03.249202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:03.465474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:03.679755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:03.894972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:04.114236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:04.340600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:04.566127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:04.778965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:04.988188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:05.202780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:05.420058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:05.634343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:05.845077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:06.062512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:06.283102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:06.504540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:06.745427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:06.976501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:07.197486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:07.420460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:07.643715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:07.870952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:08.089271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:08.302725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:08.516603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:08.732126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:08.948770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:09.165578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:09.385818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:09.603075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:09.819694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:10.038189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:10.256344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-24T16:20:06.305153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-24T16:20:06.417924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-24T16:20:06.892216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-02-24T16:20:07.025774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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2022-02-24T16:19:53.240399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:55.987066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-24T16:19:56.598548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFHEAT_ARASD_VALTAX_VALSD1SD2TIFACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDLAND_TYPE_IDBLOCK_NUMLOT_NUMYEAR_BUILTYEAR_EFFECTIVEYEAR_HOMESTEAD
08000008010001001987-08-01IQ0150000.0WD1985-11-0119859 ANGEL LNODESSA335563.02.02.01.01.0565190.0174976.0384856.05358.02617.0418262.0368262.000000005.058780211007.0U01271700100000000001.11996-01-012008-01-012016-01-01
19000008010001001985-11-01VQ0124000.0WD1985-11-0119859 ANGEL LNODESSA335563.02.02.01.01.0565190.0174976.0384856.05358.02617.0418262.0368262.000000005.058780211007.0U01271700100000000001.11996-01-012008-01-012016-01-01
211000009010001002021-10-27IQ01750000.0WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.01572.0453092.0453092.000000004.438490211007.0U01271700100000000002.11976-01-011998-01-011973-01-01
314000009010001001997-05-01IQ01169900.0WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.01572.0453092.0453092.000000004.438490211007.0U01271700100000000002.11976-01-011998-01-011973-01-01
420000010000001001988-06-01IQ0152500.0WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.02143.0173560.0123560.000000000.992559211007.0U01271700100000000003.01926-01-011973-01-011994-01-01
521000010000001001983-02-01IQ0130000.0WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.02143.0173560.0123560.000000000.992559211007.0U01271700100000000003.01926-01-011973-01-011994-01-01
622000010000101001998-08-01VQ0125000.0WD1979-06-017020 COUNTY LINE RDUnincorporated33556-3.02.01.01.01.0379136.0100470.0250471.028195.01919.0210422.0160422.000000001.362980211007.0U01271700100000000004.12001-01-012011-01-012002-01-01
723000010000201002012-06-19IQ02272500.0WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.02971.0223410.0173410.000000001.309540211007.0U01271700100000000004.21987-01-012003-01-012013-01-01
824000010000201002004-06-01IQ01207500.0WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.02971.0223410.0173410.000000001.309540211007.0U01271700100000000004.21987-01-012003-01-012013-01-01
925000010000201001999-02-01IQ01145000.0WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.02971.0223410.0173410.000000001.309540211007.0U01271700100000000004.21987-01-012003-01-012013-01-01

Last rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFHEAT_ARASD_VALTAX_VALSD1SD2TIFACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDLAND_TYPE_IDBLOCK_NUMLOT_NUMYEAR_BUILTYEAR_EFFECTIVEYEAR_HOMESTEAD
8470922047201209435000001002000-01-10IQ0185000.0WD1971-01-01704 E DREW STPLANT CITY335633.02.01.01.01.0217427.075200.0139350.02877.01195.0116985.0116985.000000000.367309221006.0P3328225EL00000500006.01990-01-012005-01-011971-01-01
8470932047208209436000001002021-06-30IQ02169000.0WD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.0840.0132090.0132090.000000000.174472221006.0P3328225EL00000500008.01938-01-011995-01-011980-01-01
8470942047211209436000001002012-12-10IQ2A35700.0WD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.0840.0132090.0132090.000000000.174472221006.0P3328225EL00000500008.01938-01-011995-01-011980-01-01
8470952047213209436000001002006-02-28IQ02123900.0WD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.0840.0132090.0132090.000000000.174472221006.0P3328225EL00000500008.01938-01-011995-01-011980-01-01
8470962047214209436000001002005-09-07IQ0175000.0WD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.0840.0132090.0132090.000000000.174472221006.0P3328225EL00000500008.01938-01-011995-01-011980-01-01
8470972047220209436005001002006-10-25IQ01200000.0WD2005-08-23710 E DREW STPLANT CITY33563-66023.02.01.01.01.0196313.040400.0155546.0367.01290.067435.025000.000000000.183654221006.0P3328225EL00000500010.02005-01-012013-01-012011-01-01
8470982047222209436005001002005-08-23VQ0225000.0WD2005-08-23710 E DREW STPLANT CITY33563-66023.02.01.01.01.0196313.040400.0155546.0367.01290.067435.025000.000000000.183654221006.0P3328225EL00000500010.02005-01-012013-01-012011-01-01
8470992047223209436010001002015-08-21IQ2A134000.0WD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.01290.099696.049696.000000000.288579221006.0P3328225EL00000500011.02006-01-012016-01-012016-01-01
8471002047226209436010001002006-04-28IQ01195000.0WD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.01290.099696.049696.000000000.288579221006.0P3328225EL00000500011.02006-01-012016-01-012016-01-01
8471012047227209436010001002005-08-23VQ0225000.0WD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.01290.099696.049696.000000000.288579221006.0P3328225EL00000500011.02006-01-012016-01-012016-01-01